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2. Parametric &
Non-parametric
Parametric
Non-Parametric
 A parameter to compare
Mean, S.D.
Normal Distribution & Homogeneity
 No parameter is compared
Significant numbers in a category plays the role
 No need of Normal Distribution & Homogeneity
 Used when parametric is not applicable.

3. Parametric &
Non-parametric
Parametric
Vs
Non-parametric
Which is good ?
If parametric is not applicable, then only we go for a non-parametric
Both are applicable, we prefer parametric. Why?
In parametric there is an estimation of values.
Null hypothesis is based on that estimation.
In non-parametric we are just testing a Null Hypothesis.

4. Normality ?
How do you check Normality ?
 The mean and median are approximately same.
 Construct a Histogram and trace a normal curve.
Example
? Level of Significance / p-value / Type I error / α
? Degree of Freedom

5. Types of variables
Independent variable
Dependent variable
Data representation
1. Continuous or Scale variable
2. Discrete variable
Nominal
Ordinal
(Categorical)

6. Decide your test

7. Decide your test

8. Paired t-test
Areas of application
>> When there is one group pre & post scores to compare.
>> In two group studies, if there is pre & post assessment, paired t is applied
to test whether there is significant change in individual group.
S = S.E. = t =
S.E.
Example

9. Unpaired/independent
t-test
Areas of application
>> When there is two group scores to compare.
(One time assessment of dependent variable).
>> In two group studies, if there is pre & post assessment, paired t is applied
to test whether there is significant change in individual group.
After this, the pre-post differences in the two groups are taken for testing.
Example

10. Areas of application
ANOVA
>> When there is more than two group scores to compare.
Group A x Group B x Group C
Post-HOC procedures after ANOVA
helps to compare the in-between groups
A x B , A x C , B x C
Similar to doing 3 unpaired t tests
Example

11. Wilcoxon Matched
Pairs
A Non-parametric procedure
>> This is the parallel test to the parametric paired t-test
 Before after differences are calculated with direction + ve or –ve
 0 differences neglected.
 Absolute differences are ranked from smallest to largest
 Identical marks are scored the average rank
 T is calculated from the sum of ranks associated with least frequent sign
 If all are in same direction T = 0
Example

12. Mann Whitney U
A Non-parametric procedure
>> This is the parallel test to the parametric unpaired t-test
 Data in both groups are combined and ranked
 Identical marks are scored the average rank
Sum of ranks in separate groups are calculated
 Sum of ranks in either group can be considered for U.
 n1 is associated with ∑R1i , n2 is associated with ∑R2j
Example

13. Median Test
A Non-parametric procedure
Similar to the cases of Mann Whitney
>> This is the parallel test to the parametric unpaired t-test
 Data in both groups are combined and median is calculated
 Contingency table is prepared as follows

14. Kruskal Walis
A Non-parametric procedure
>> This is the parallel test to the parametric ANOVA
>> ANOVA was an extension of 2-group t-test
>> Kruskal Walis is an extension of Mann Whitney U
 Data in all groups are combined and ranked
 Identical marks are scored the average rank
Sum of ranks in separate groups are calculated
Areas of application
>> Areas similar to ANOVA
>> Comparison of dependent variable between categories in a
demographic variable
Example

15. Mc Nemar’s Test
Areas of application
>> Similar to the parametric paired t-test, but the dependent variable
is discrete, qualitative.

16. Contact
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